Account Executive

About the Company

Models are what they eat. But a large portion of training compute is wasted training on data that are already learned, irrelevant, or even harmful, leading to worse models that cost more to train and deploy.

At Datology

AI, we’ve built a state of the art data curation suite to automatically curate and optimize petabytes of data to create the best possible training data for your models. Training on curated data can dramatically reduce training time and cost (7-40x faster training depending on the use case), dramatically increase model performance as if you had trained on >10x more raw data without increasing the cost of training, and allow smaller models with fewer than half the parameters to outperform larger models despite using far less compute at inference time, substantially reducing the cost of deployment.

For more details, check out our recent blog posts sharing our high-level results for text models and image-text models.

We raised a total of $57.5M in two rounds, a Seed and Series A. Our investors include Felicis Ventures, Radical Ventures, Amplify Partners, Microsoft, Amazon, and AI visionaries like Geoff Hinton, Yann LeCun, Jeff Dean, and many others who deeply understand the importance and difficulty of identifying and optimizing the best possible training data for models. Our team has pioneered this frontier research area and has the deep expertise on both data research and data engineering necessary to solve this incredibly challenging problem and make data curation easy for anyone who wants to train their own model on their own data.

This role is based in Redwood City, CA. We are in office 4 days a week.

We are looking for an Account Executive to drive new business with strategic enterprise and mid-market customers. You will own the full sales cycle, from sourcing and qualification through close, and play a central role in shaping how Datology

AI goes to market.

This is a hands‑on, outbound‑driven role for someone excited to sell a technically sophisticated product to machine learning, data, and infrastructure teams. You will work closely with founders, research leadership, and solutions engineering to run technical evaluations, navigate complex buying committees, and convert early customer interest into long‑term platform adoption.

You'd bring recent prospecting experience and a strong history of selling into ML‑driven organizations. You’re comfortable navigating ambiguity, excited to build new territories and playbooks, and eager to influence messaging, ideal customer profiles, and sales motion as the company grows.

• Run complex, technical sales cycles end‑to‑end, from first touch through close
• Sell a highly technical, ML‑heavy product to sophisticated buyers by confidently engaging on concepts like model training, data quality, and ML infrastructure, while translating deep technical value into clear business outcomes
• Generate pipeline from scratch through hands‑on outbound, account mapping, and technical persona–driven prospecting
• Design, test, and continuously iterate on outbound strategies, from ICP definition and account selection to messaging, sequencing, and channels
• Build trusted, hands‑on relationships with deeply technical customers, including ML engineers, research scientists, infrastructure leads, and platform teams
• Navigate complex stakeholder environments and multi‑thread deals across engineering, finance, security, and exec teams
• Translate Datology
  
  AI’s technical capabilities into clear business value around training cost reduction, model quality, and faster iteration cycles
• Partner closely with internal stakeholders to run technical evaluations and proof‑of‑concepts
• Provide structured feedback to internal teams based on field learnings

• 5+ years of sales experience with a consistent record of achieving quota and building pipeline from scratch; highly comfortable with outbound prospecting across cold calls, email, and social
• Experience selling into ML/AI, data, infra, or adjacent technical teams; understands how modern ML orgs evaluate tools and run pilots
• Thrive in early‑stage environments where playbooks are still taking…